Speech processing techniques are disclosed that enable determining a text representation of alphanumeric sequences in captured audio data. Various implementations include determining a contextual biasing finite state transducer (FST) based on contextual information corresponding to the captured audio data. Additional or alternative implementations include modifying probabilities of one or more candidate recognitions of the alphanumeric sequence using the contextual biasing FST, where the FST further comprises a grammar as well as a speller finite state transducer.
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2. The method of claim 1, wherein the ASR model is a recurrent neural network transducer (RNN-T) model, and wherein the ASR engine further comprises a beam search portion.
8. The method of claim 1, wherein the audio data is captured via one or more microphones of a client device.
10. The method of claim 1, wherein the alphanumeric sequence includes at least one number and includes at least one letter.
11. The method of claim 1, wherein the ASR model portion of the ASR engine is an end-to-end speech recognition model.
12. The method of claim 1, wherein the ASR engine is trained using a set of training instances, and wherein the alphanumeric sequence is not in the set of training instances.
13. The method of claim 1, wherein the ASR engine is trained using a set of training instances, and wherein the alphanumeric sequence occurs a number of times, in the set of training instances, that is below a threshold value.
16. The computing system of claim 15, wherein the ASR model is a recurrent neural network transducer (RNN-T) model, and wherein the ASR engine further comprises a beam search portion.
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January 17, 2020
March 26, 2024
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